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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.01504 |
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| _version_ | 1866918150314917888 |
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| author | Machado, Erika M. Herrera Andersen, Jakob L. Fagerberg, Rolf Flamm, Christoph Merkle, Daniel Stadler, Peter F. |
| author_facet | Machado, Erika M. Herrera Andersen, Jakob L. Fagerberg, Rolf Flamm, Christoph Merkle, Daniel Stadler, Peter F. |
| contents | The MØD computational framework implements rule-based generative chemistries as explicit transformations of graphs representing chemical structural formulae. Here, we expand MØD by a stochastic simulation module that simulates the time evolution of species concentrations using Gillespie's well-known stochastic simulation algorithm (SSA). This module distinguishes itself among competing implementations of rule-based stochastic simulation engines by its flexible network expansion mechanism and its functionality for defining custom reaction rate functions. It enables direct sampling from actual reactions instead of rules. We present methodology and implementation details followed by examples which demonstrate the capabilities of the stochastic simulation engine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rule-Based Gillespie Simulation of Chemical Systems Machado, Erika M. Herrera Andersen, Jakob L. Fagerberg, Rolf Flamm, Christoph Merkle, Daniel Stadler, Peter F. Molecular Networks Chemical Physics The MØD computational framework implements rule-based generative chemistries as explicit transformations of graphs representing chemical structural formulae. Here, we expand MØD by a stochastic simulation module that simulates the time evolution of species concentrations using Gillespie's well-known stochastic simulation algorithm (SSA). This module distinguishes itself among competing implementations of rule-based stochastic simulation engines by its flexible network expansion mechanism and its functionality for defining custom reaction rate functions. It enables direct sampling from actual reactions instead of rules. We present methodology and implementation details followed by examples which demonstrate the capabilities of the stochastic simulation engine. |
| title | Rule-Based Gillespie Simulation of Chemical Systems |
| topic | Molecular Networks Chemical Physics |
| url | https://arxiv.org/abs/2509.01504 |